Sleep Posture Detection Using an Accelerometer Placed on the Neck

Abdulsadig, Rawan S.; Singh, Sukhpreet; Patel, Zaibaa; Rodriguez‐Villegas, Esther

doi:10.1109/embc48229.2022.9871300

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…This work is an extension of our previous preliminary work where the classification performance of the four main sleep postures using the decision tree (DT) and extra-trees classifier (ET) model types was reported, while keeping the sampling rate and window size fixed at 100 Hz and 5 seconds, respectively. In that study, the concept of sleep posture detection through data-driven modelling approaches using accelerometry data collected from the neck via a wearable device was first proposed, and its feasibility was highlighted [30]. This paper extends the previous work in two main ways: The first is the exploration of the impact of changing the sampling rate and the window size on the models' classification performance, detection and prediction time as well as memory consumption.…”

Section: Related Workmentioning

confidence: 79%

Sleep Posture Monitoring Using a Single Neck- Situated Accelerometer: A Proof-of-Concept

Abdulsadig

Rodriguez‐Villegas

2023

IEEE Access

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Sleep position identification and monitoring is important in the context of certain healthcare conditions, such as obstructive sleep apnoea and epilepsy. Many studies have thoroughly investigated automatic sleep detection using various sensing channels located in optimum body locations. However, this has not been the case for detection using physiological data acquired from a single sensing channel on the neck. In certain healthcare contexts the neck can, however, be an attractive location despite being suboptimal for position monitoring; the reason being that it enables better extraction of more critical biomarkers from other sensing modalities, making possible multimodal monitoring using just one wearable. This work focuses on investigating methods of automatic sleep position detection using one wearable channel of accelerometry data sensed on the neck. Three different models are explored. These are based on: decision trees (DT), extra-trees classifier (ET) and long-short term memory neural networks (LSTM-NN). The paper also investigates for the first time what would be optimum design choices when considering that wearables are power and memory-constrained, but performance in the type of healthcare applications where a single location multimodal sensing is important must not be compromised. This includes looking into how changing the sampling rate and window sizes would affect the performance of the different models. It is demonstrated that a sampling rate as low as 5 Hz, and a window size as short as 1 second, still lead to high classification performance (around 0.945, 0.975 and 0.965 mean f1-score when using the DT, ET and LSTM-NN models, respectively, and at least 98% average accuracy in all three models); and that the DT model occupies the least memory space (1.765 KB) and takes the least mean prediction time across all window sizes (around 0.8 ms).

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Section: Related Workmentioning

confidence: 79%

Sleep Posture Monitoring Using a Single Neck- Situated Accelerometer: A Proof-of-Concept

Abdulsadig

Rodriguez‐Villegas

2023

IEEE Access

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“…For vital signs based classifier, vital sign signals acquired by MEMS IMU, ElectroCardioGraphy (ECG), BCG, SCG or GCG device are fed into this classifier to identify the sleep posture. After pre-processing of the vital sign signals, K-nearest neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), ExtraTree (ET), K-means clustering, Swin Transformer (ST), SVM, CNN are adopted for feature extraction and sleep posture recognition, and the detection accuracy ranges from 80.8 % to 99.67 % [ 32 , [42] , [43] , [44] , [45] , [46] ]. The detection accuracies of some vital signs based classifiers are very high, especially for machine learning models.…”

Section: Introductionmentioning

confidence: 99%